Temporal Trends for Patients Hospitalized With Atrial Fibrillation in the United States: An Analysis From the National Inpatient Sample (NIS) Database 2011-2018

Background: Atrial fibrillation (AF) has historically been a growing burden on the global public health system. Previously, literature on the trends associated with AF-related hospitalizations has been published. However, there seems to be a gap in up-to-date information, notably within the last decade. Purpose: This study aims to investigate the trends, outcomes, and factors associated with AF hospitalization and the continued impact of AF on the United States health system. Methods: Patient data were collected from the years 2011 to 2018 from the National Inpatient Sample (NIS) database using the International Classification of Diseases (ICD)-9 and ICD-10 codes. We selected patients hospitalized with a diagnosis of AF. Descriptive statistics, statistical analysis, and Mann-Whitney U testing were employed to compare continuous dichotomous variables. After respective adjustments, multivariate hierarchical logistic regression was used to establish mortality rates, length of stay (LOS), and hospital charges. Results: The study included 509,305 patients hospitalized with a primary diagnosis of unspecified AF. The mean age of patients hospitalized with AF was 71 years. AF hospitalizations were slightly higher in women as compared to men (51.7% vs. 48.2%). The predominant race involved was Caucasians at 77.9% followed by African Americans and Hispanics at 7.4% and 5.4%, respectively. The three most frequent coexisting conditions noted were hypertension (69.9%), diabetes mellitus (24.3%), and chronic obstructive pulmonary disease (16.4%). Medicare/Medicaid was the primary payer associated with the majority of AF hospitalizations at 72.6%. Overall in-hospital mortality associated with AF hospitalizations was 0.96%. Comorbid conditions conferring the highest mortality risks included coagulopathies (644%) and cerebral vascular accidents (597%). Mean LOS was found to be 3.35 days. Hospitalization charges increased year-over-year and correlated with an increase in the national burden of cost for these patients of $3.6 billion. Conclusions: Our study investigates the national trends surrounding AF hospitalizations. Overall in-hospital mortality rates appear to be stable as compared to prior years and past literature. Comorbid conditions conferring significantly higher mortality rates included coagulopathies, cerebral vascular accidents, acute kidney injury, and end-stage renal disease. Additionally, suboptimal insurance status was also associated with increased mortality risk. The cost of hospitalization in AF patients has increased steadily, conferring a $3.6 billion burden on the US healthcare system.


Introduction
Atrial fibrillation (AF) has historically been a growing burden on the global public health system [1]. The rising incidence and prevalence of AF in the United States continue to stress the public from both a medical and economic standpoint [1,2]. In developed nations, the number of men and women affected by AF is projected to double over the next two decades [3]. The prevalence of AF is projected to increase to 15.9 million by the year 2050 [4]. AF is notably associated with stroke, structural heart disease, congestive heart failure, and impaired quality of life [5][6][7]. This results in significant demand on the healthcare system, along with a substantial economic burden. Previously, literature on the trends associated with AF-related hospitalizations has been published [4,[8][9][10]. However, there seems to be a gap in up-to-date information, most notably within the last decade. To continue to support the national healthcare system, it is imperative that researchers and clinicians stay diligent in their understanding of the current trends associated with AFrelated hospitalizations. This study aims to identify particular patterns surrounding AF-related hospitalizations across its various subpopulations, association with various comorbid diagnoses, in-hospital 1 1 1 1 1 1 mortality rates, impact on the length of stay (LOS), and updated understanding surrounding current trending AF-related billing and coding.

Materials And Methods
The National Inpatient Sample (NIS) of the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality (AHRQ), was used for this study [11]. It is the largest inpatient hospitalization-related healthcare dataset in the United States. NIS includes data from all states taking part in HCUP, which approximates 97% of the US population (NIS Database Documentation) [11]. It utilized billing International Classification of Diseases (ICD) codes to confer patient diagnoses while supplying socioeconomic data. It represents a 20% sample from inpatient hospitalizations every year. Patient data were collected for the years 2011-2018 from this database and we identified hospitalized patients with a documented diagnosis of unspecified AF in US hospitals using ICD-9 and ICD-10 codes in congruence with previously widely accepted literature [4]. Descriptive statistics were used to summarize the baseline characteristics of our patient population. We compared baseline demographics including age, sex, race, geographic location, and primary expected payer in AF-related hospitalizations. Patients younger than 18 years were excluded. The primary outcome was AF-related in-hospital mortality. Secondary outcomes were comorbid conditions, socioeconomic factors associated with AF, mean LOS, and total hospitalization charges ($). Statistical analysis was performed with Mann-Whitney U testing to compare continuous dichotomous variables. After adjusting for hospital size, type, and transfer status, a hierarchical multivariate regression model was used to calculate adjusted odds ratios (AOR) with 95% confidence intervals (CIs). Pvalue < 0.05 was considered significant. We used IBM SPSS Statistics 28.0 software (IBM Corp., Armonk, NY) for all statistical analysis.

Length of stay, cost of hospitalization, and disposition
LOS, cost of hospitalization, and primary expected payer findings subdivided by mortality are summarized in Table 2. The mean LOS was found to be 3. Disparities among dispositions were noted, as seen in Table 3. African Americans had a 27.6% higher chance of non-routine discharge (against medical advice, nursing home, skilled nursing facility, long-term acute care hospital, etc.). The female sex was noted to have a 28.9% higher chance of non-routine discharge. Among comorbidities, CVA conferred the highest risk of non-routine discharge at 528%, followed by coagulopathy at 207.3% and AKI at 199.8%. Finally, larger bed-size hospitals were known to confer a smaller risk of non-routine discharge ( Table 4).

Discussion
Our study highlights up-to-date data surrounding the current state of AF-related hospitalizations in the United States. Overall in-hospital mortality of AF hospitalizations appears unchanged [4,8]. We did observe in-hospital mortality rates to substantially increase over the more recent years (2016-2018). However, during the same time frame, a significant drop in patients with a primary diagnosis of AF was also noted. This decrease in the patient population may have falsely increased the mortality rate. Women continue to suffer from AF-related hospitalizations at a higher rate than men [8][9][10]. However, there was no statistically significant difference in mortality outcomes. In regards to age, previous findings seem to be replicated, as increased in-hospital mortality rates were seen with increased age [12].
The three most common comorbidities seen with AF hospitalizations continue to be hypertension, diabetes, and COPD [4,6,13]. Obstructive sleep apnea was additionally prevalent among our patient population, signifying the risk conferred from pulmonary pathology contributing to AF hospitalization. The highest mortality rates in AF hospitalizations were seen in patients also suffering from coagulopathies and CVA. End-stage renal disease (ESRD) and AKI additionally conferred significantly higher mortality risk. The latter may be explained by patients who present with AF with a rapid ventricular response and concurrent hemodynamic compromise, worsening their overall prognosis. Coagulopathies and CVA may have increased mortality risk among our patients due to possible supratherapeutic anticoagulation or failed anticoagulation therapy leading to eventual development or worsening of CVA. However, NIS does not afford enough granular detail to determine various pharmacotherapies associated with each patient's hospitalization.
Current projections expect the national health expenditures to grow by an overall rate of 5.4% for the years 2019-2028 [14]. Previous literature cites mean hospitalization costs in the $6000-$8000 range [4,15]. We found the mean hospitalization cost to be substantially higher than previously described (upwards of $30,000). Furthermore, mean hospitalization cost was found to increase roughly $7,000 over the same timeframe. Part of this increase may be explained by increased LOS, a mean of 3.35 days in our study versus less than three days in prior studies [4]. However, although we did observe an increase in LOS, due to differences in our underlying patient population, we are unable to draw specific conclusions due to hospital differences in patient complexity.
We observed socioeconomic factors modulating mortality risk as well. Patients with Medicaid as a primary payer had a 63% increased risk of mortality compared to Medicare. Self-pay, no charge, and other payer statuses also conferred a higher risk of mortality. This illustrates that suboptimal insurance status is associated with increased mortality risk. We believe these differences may also play a role in explaining increased risk in mortality with coagulopathies, as patients with suboptimal insurance status are more prone to be on warfarin therapy, thus increasing the risk for bleeding complications. Finally, private pay patients were noted to have a 49% lower risk of non-routine discharge when compared to Medicaid and Medicare patients, possibly indicating an initially healthier patient population.

Limitations
Although our study utilizes a large, well-known administrative database, it is not without its limitations. The primary diagnosis code ran was unspecified AF. It is entirely possible that patients presenting with and admitted for other reasons could qualify for our study, however, they had a different diagnosis coded in the primary position, thus excluding them from our study population. Furthermore, although we investigated the use of other more specified AF codes (notably paroxysmal AF, other persistent AF, and long-term persistent AF), we did not factor these patients into our original/total patient population. Essentially, we only included patients hospitalized with a primary diagnosis of unspecified AF, keeping in congruence with prior published studies [4]. Moreover, we sought to specifically focus on patients without a previously established diagnosis of AF. We believe utilizing a primary diagnosis of unspecified AF was the best way to accomplish this. Thus, when comparing our study to existing literature, the study's sample size and subsequent power may be less than previous studies [3]. Additionally, the NIS is known for logging each hospitalization as its own entry. Thus, it is impossible to separate index cases from readmissions. Even more so, many patients admitted with AF originally present with AF with a rapid ventricular response; a diagnosis that does not have a specific corresponding ICD-9 or ICD-10 code. Lastly, because our study investigated AF hospitalizations and does not include outpatient data or long-term follow-up outcomes, overall incidence, prevalence, and mortality rates may be underestimated. However, despite the aforementioned limitations, our study offers some strengths when compared to previous literature on the same topic: updated data for years not included in prior studies, a more specific study population with a primary diagnosis of AF, and improved analytics surrounding causal associations given logistical regression analysis.
It must be noted that the patient population experienced a significant drop-off from 2016 to 2018. This was originally felt to be due to more precise diagnosis coding; however, after investigating more specific ICD-10 codes surrounding AF (paroxysmal, persistent, long-standing persistent, and permanent), an additional 97 patients were found. Thus, the initial drop in patients hospitalized for unspecified AF cannot be explained by the above. One explanation may be the utilization of comorbid diagnosis in the primary position to increase hospitalization reimbursement rates with the coding of unspecified AF in subsequent positions.

Conclusions
Here, we report updated data surrounding trends of patients suffering AF hospitalization. Overall inhospital mortality rates appear stable when compared to prior studies. Comorbid conditions conferring significantly higher mortality rates included coagulopathies, CVA, AKI, and ESRD. Suboptimal insurance status was also associated with increased mortality risk. The cost of hospitalization has steadily increased year over year. It is imperative that continued efforts to both prevent and manage AF hospitalizations be developed, studied, and refined to reduce the burden of AF on the US healthcare system.

Additional Information Disclosures
Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

Conflicts of interest:
In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.